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# pylint: disable=invalid-name, unused-argument
"""Compute definition for conv2d with cuda backend"""
from tvm import te
from tvm import autotvm
from tvm.autotvm.task.space import OtherOptionEntity
from tvm.contrib import cudnn

from .. import nn, generic
from ..nn.util import get_pad_tuple
from ..util import get_const_tuple, traverse_inline
from .conv2d_direct import schedule_direct_cuda
from .conv2d_nhwc import schedule_conv2d_nhwc_direct


@autotvm.register_topi_compute("conv2d_nchw.cuda")
def conv2d_nchw(cfg, data, kernel, strides, padding, dilation, out_dtype='float32'):
    """Compute conv2d with NCHW layout"""
    return nn.conv2d_nchw(data, kernel, strides, padding, dilation, out_dtype)


@autotvm.register_topi_schedule("conv2d_nchw.cuda")
def schedule_conv2d_nchw(cfg, outs):
    """Create the schedule for conv2d_nchw"""
    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
    s = te.create_schedule([x.op for x in outs])

    def _callback(op):
        if op.tag == 'conv2d_nchw':
            schedule_direct_cuda(cfg, s, op.output(0))

    traverse_inline(s, outs[0].op, _callback)
    return s


@autotvm.register_topi_compute("conv2d_nhwc.cuda")
def conv2d_nhwc(cfg, data, kernel, strides, padding, dilation, out_dtype='float32'):
    """Compute conv2d with NHWC layout"""
    return nn.conv2d_nhwc(data, kernel, strides, padding, dilation, out_dtype)


@autotvm.register_topi_schedule("conv2d_nhwc.cuda")
def schedule_conv2d_nhwc(cfg, outs):
    """Create the schedule for conv2d_nhwc"""
    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
    s = te.create_schedule([x.op for x in outs])
    def _callback(op):
        if op.tag == 'conv2d_nhwc':
            schedule_conv2d_nhwc_direct(cfg, s, op.output(0))
    traverse_inline(s, outs[0].op, _callback)
    return s


@autotvm.register_topi_compute("conv2d_cudnn.cuda")
def conv2d_cudnn(cfg, data, kernel, strides, padding, dilation, groups=1,
                 layout='NCHW', out_dtype='float32'):
    """Compute conv2d using CuDNN library"""
    if layout == 'NCHW':
        tensor_format = 0 # CUDNN_TENSOR_NCHW
        N, _, H, W = get_const_tuple(data.shape)
    elif layout == 'NHWC':
        tensor_format = 1 # CUDNN_TENSOR_NHWC
        N, H, W, _ = get_const_tuple(data.shape)
    else:
        raise ValueError("Unsupported layout %s in cudnn" % layout)
    CO, CI, KH, KW = get_const_tuple(kernel.shape)

    # handle dilation
    stride_h, stride_w = (strides, strides) if isinstance(strides, int) else strides
    dilation_h, dilation_w = (dilation, dilation) if isinstance(dilation, int) else dilation

    if isinstance(padding, (list, tuple)) and len(padding) == 4 and \
            (padding[0] != padding[2] or padding[1] != padding[3]):
        raise ValueError("Cudnn doesn't support asymmetric padding.")
    pt, pl, pb, pr = get_pad_tuple(padding, (KH, KW))
    OH = (H + pt + pb - KH) // stride_h + 1
    OW = (W + pl + pr - KW) // stride_w + 1
    cfg.add_flop(groups * 2 * N * OH * OW * CO * CI * ((KH - 1) * dilation_h + 1) * \
                 ((KW - 1) * dilation_w + 1))

    if data.dtype == "int8" or kernel.dtype == "int8":
        if layout == 'NCHW':
            raise ValueError("NCHW layout do not support int8 in cudnn")
        dtype = "int32"
    else:
        dtype = data.dtype

    cfg.define_knob('algo', range(8))
    if cfg.is_fallback: # Let CUDNN choose the best algo
        cfg['algo'] = OtherOptionEntity(-1)

    return cudnn.conv_forward(data,
                              kernel,
                              [pt, pl], # cudnn padding pt, pl on both sides of input
                              [stride_h, stride_w],
                              [dilation_h, dilation_w],
                              conv_mode=1,
                              tensor_format=tensor_format,
                              algo=cfg['algo'].val,
                              conv_dtype=dtype,
                              groups=groups)


@autotvm.register_topi_schedule("conv2d_cudnn.cuda")
def schedule_conv2d_cudnn(cfg, outs):
    """Create the schedule for conv2d_cudnn"""
    return generic.schedule_extern(outs)
